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Assessing AI-TPACK readiness in mathematics teacher education: The role of self-efficacy and teaching beliefs

● high · 2026-08-16 · teacher-education, tpack, math-education, self-efficacy, mathematics-teacher-education

Synthesis: Xie and Luo (2026) develop and validate a domain-specific AI-TPACK instrument tailored to the unique pedagogical and logical demands of Math Education, then survey 412 Chinese mathematics Teacher Education students (MTES) across seven universities (289 female, 123 male; 262 senior undergraduates, 105 first-year and 45 second-year graduate students). Their AI-TPACK readiness is currently at a preliminary stage, with all six construct means falling in the medium range (3.76–5.33 on 7-point scales): teaching beliefs were highest (M = 5.24) and AI-TK lowest (M = 4.23). A structural equation model shows that Self Efficacy is a significant positive predictor of AI-TK (β = 0.69), AI-TCK (β = 0.78), and AI-TPK (β = 0.73), whereas strong traditional teaching beliefs act as a weak cognitive barrier (negative paths to AI-TCK and AI-TPK), and AI-TK's influence on AI-TPACK is fully mediated through AI-TPK and AI-TCK. The findings provide empirical evidence for redesigning mathematics teacher training to address both technical proficiency and psychological readiness.

Key Findings

Domain-specific instrument. A 24-item AI-TPACK scale across six dimensions (AI-TK, AI-TCK, AI-TPK, AI-TPACK, self-efficacy, teaching beliefs) was developed following DeVellis & Thorpe's framework, starting from a 35-item pool rated on 7-point Likert scales and screened by a five-expert panel (three educational-technology professors, two mathematics-education specialists) that removed or rephrased five ambiguous items. EFA on a pilot sample of 128 MTES from one university (KMO = .930, Bartlett's test significant, p < .001; item-to-participant ratio 1:4.3) then CFA confirmed the structure, with final factor loadings of .485–.863, 84.62% total variance explained, and Cronbach's alphas of .883 (AI-TK), .917 (AI-TPK), .919 (AI-TCK), .930 (AI-TPACK), .909 (self-efficacy), and .894 (teaching beliefs).

Preliminary readiness. The final survey of 412 MTES (item-to-participant ratio ≈ 1:17, satisfying the 10–20-observations-per-parameter rule) revealed AI-TPACK readiness at a preliminary stage: teaching beliefs were highest (M = 5.24) and AI-TK lowest (M = 4.23), with AI-TCK (M = 4.53) the highest AI-TPACK component. AI use was largely "consumption-oriented" (searching answers, designing exam questions, drafting basic lesson plans) rather than advanced (e.g., VR for 3D geometry, intelligent student-observation systems), which the authors link to the tension between heuristic exploration and exam-oriented performance in Chinese mathematics education.

No grade-level differences in AI-TPACK. ANOVA found no significant differences across senior undergraduates and first- and second-year graduate students for AI-TK (F = 1.79, p = .170), AI-TCK (F = 2.14, p = .119), AI-TPK (F = 0.37, p = .708), or AI-TPACK (F = 0.83, p = .435), although self-efficacy (F = 4.68, p = .010) and teaching beliefs (F = 7.26, p < .001) did differ; teaching experience also did not significantly enhance AI-TPACK. The authors attribute the stagnation to fragmented, informal internet-based learning and limited AI exposure during internships.

Self-efficacy as predictor. In the final AI-TPACK-SEM (χ²/df = 3.35, RMSEA = 0.078, NFI = 0.915, CFI = 0.938, TLI = 0.927), self-efficacy strongly and positively predicted AI-TK (β = 0.69), AI-TCK (β = 0.78), and AI-TPK (β = 0.73), supporting all H1 paths and reflecting a psychological catalyst that reduces perceived complexity of AI tools.

Teaching beliefs as cognitive barrier. Strong traditional teaching beliefs showed weak negative associations with AI-TCK (β = −0.03) and AI-TPK (β = −0.15), contrary to the H2 hypotheses, interpreted via second-order barriers and the conflict between traditional mathematical rigor and AI's perceived unpredictability. AI-TK did not directly predict AI-TPACK but was mediated through AI-TPK (β = 0.30) and AI-TCK (β = 0.35), which in turn predicted overall AI-TPACK (β = 0.59 and 0.64) — consistent with Ouyang et al.'s "know-how/know-why/know-how-to-teach" synthesis.

Implication. Mathematics teacher training must be redesigned to address both technical proficiency and psychological readiness (self-efficacy and beliefs), embedding AI-pedagogical training continuously across the four-year curriculum rather than as a single elective, and linking to the wiki's Teacher Education and Math Education concepts. Limitations include cross-sectional self-reported data with potential social-desirability bias, restriction to the Chinese mathematics-education context, and exploratory pruning of non-significant paths (H2b, H3c) pending larger-sample validation.

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Citation

Xie, M., & Luo, L. (2026). Assessing AI-TPACK readiness in mathematics teacher education: The role of self-efficacy and teaching beliefs. Computers and Education Open, 100375. https://doi.org/10.1016/j.caeo.2026.100375